Mass Deportations Would Devastate Employment Across Multiple States

Projected employment impacts 2025-2029 vs. 2024 baseline

MakeoverMonday
Data Visualization
R Programming
2025
A MakeoverMonday data visualization reimagining the Economic Policy Institute’s analysis of projected employment impacts from mass deportations. Using R and ggplot2, I transformed a basic heat map into a compelling two-chart story showing both the scale of job losses by state and the impact on different worker types. Features include color-coded impact tiers, strategic labeling, and unified design system for publication-ready visualizations.
Author

Steven Ponce

Published

July 15, 2025

Original

The original visualization Figure B, Trump’s deportations will reduce employment in every state. Overall and construction employment losses for 4 million national deportations over four years, by state comes from Economic Policy Institute

Original visualization

Makeover

Figure 1: Two side-by-side charts showing projected employment impacts from mass deportations 2025-2029. Left chart: horizontal bar chart ranking top 20 states by total job losses, with California leading at 1.1M jobs, followed by Texas at 865K. Color coding indicates impact tiers, ranging from 500K+ jobs (red) to under 100K (gray). Right chart: Dumbbell plot showing the same 20 states, with blue and red dots representing job losses for U.S.-born and immigrant workers, respectively. This demonstrates that both worker types are affected in every state. Data source: Economic Policy Institute.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  scales,         # Scale Functions for Visualization
  glue,           # Interpreted String Literals
  patchwork       # The Composer of Plots
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  12,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
deportations_raw <- readxl::read_excel(
  here::here('data/MakeoverMonday/2025/Trump Deportations.xlsx')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(deportations_raw)
skimr::skim(deportations_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

deportations <- deportations_raw |>
  filter(state != "United States") |> # Remove national totals
  mutate(
    impact_tier = case_when(
      total_level >= 500000 ~ "mega",
      total_level >= 200000 ~ "high",
      total_level >= 100000 ~ "medium",
      TRUE ~ "lower"
    )
  )

### |-  Plot 1 Data ----
barchart_data <- deportations |>
  top_n(20, total_level) |>
  mutate(state = fct_reorder(state, total_level))

### |-  Plot 2 Data ----
# Get the same top 20 states as P1 in the same order
p1_states_ordered <- deportations |>
  top_n(20, total_level) |>
  arrange(desc(total_level)) |>
  pull(state)

dumbbell_data <- deportations |>
  filter(state %in% p1_states_ordered) |>
  mutate(state = factor(state, levels = rev(p1_states_ordered))) |> # rev() for ggplot ordering
  pivot_longer(cols = c(u_s_born, immigrant), names_to = "worker_type", values_to = "jobs_lost") |>
  mutate(
    worker_type = case_when(
      worker_type == "u_s_born" ~ "U.S.-Born Workers",
      worker_type == "immigrant" ~ "Immigrant Workers"
    )
  )
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = list(
  # Scale tiers for bar chart
  mega = "#c0392b", # mega impact (500K+)
  high = "#f39c12", # high impact (200-500K)
  medium = "#3498db", # medium impact (100-200K)
  lower = "#95a5a6",

  # Worker types for dumbbell
  immigrant = "#e74c3c", # immigrant workers
  us_born = "#3498db", # US-born workers
  connector = "#bdc3c7" # connecting lines
))

### |-  titles and caption ----
title_text <- paste0("Mass Deportations Would Devastate Employment Across Multiple States")
subtitle_text <- paste0("Projected employment impacts ", current_year, "-", current_year + 4, " vs. ", current_year - 1, " baseline")

# Create caption
caption_text <- create_mm_caption(
   mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("<br>Economic Policy Institute (July ", current_year, ") | Projections based on 1M annual deportations vs. 330K baseline")
)


### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      size = rel(1.2), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), hjust = 0.5, family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9), lineheight = 0.9,
      margin = margin(t = 5, b = 20)
    ),

    # Legend formatting
    legend.position = "top",
    legend.direction = "horizontal",
    legend.box.margin = margin(b = 10),
    legend.margin = margin(b = 5),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(face = "bold", size = rel(0.85)),
    axis.title.y = element_text(face = "bold", size = rel(0.85)),
    axis.text = element_text(size = rel(0.85), family = fonts$subtitle, color = colors$text),

    # Legends

    # Grid lines
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |-  Plot 1  bar chart ----
p1 <- barchart_data |>
  ggplot(aes(x = total_level, y = state, fill = impact_tier)) +
  # Geoms
  geom_col(alpha = 0.9, width = 0.7) +
  geom_text(
    aes(label = case_when(
      total_level >= 1000000 ~ paste0(round(total_level / 1000000, 1), "M"),
      total_level >= 1000 ~ paste0(round(total_level / 1000, 0), "K"),
      TRUE ~ scales::comma(total_level)
    )),
    hjust = -0.1,
    size = 3,
    color = "gray30"
  ) +
  # Scales
  scale_x_continuous(
    labels = comma_format(suffix = "K", scale = 1 / 1000),
    expand = c(0, 0, 0.15, 0), # More space for labels
    breaks = scales::pretty_breaks(n = 5)
  ) +
  scale_fill_manual(
    values = colors$palette,
    labels = c(
      "mega" = "500K+ jobs",
      "high" = "200-500K jobs",
      "medium" = "100-200K jobs",
      "lower" = "<100K jobs"
    ),
    name = NULL
  ) +
  # Labs
  labs(
    title = "Scale of Impact: CA and TX Dominate Job Losses",
    subtitle = NULL,
    x = "Projected Job Losses (thousands)",
    y = NULL
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
  ) +
  guides(fill = guide_legend(
    title.position = "top",
    title.hjust = 0.5,
    nrow = 1,
    override.aes = list(alpha = 0.9)
  ))

### |-  Plot 2  dumbbell chart ----
p2 <- dumbbell_data |>
  ggplot(aes(y = state)) +
  # Geoms
  geom_segment(
    data = deportations |>
      filter(state %in% p1_states_ordered) |>
      mutate(state = factor(state, levels = rev(p1_states_ordered))),
    aes(x = u_s_born, xend = immigrant, y = state, yend = state),
    color = colors$palette[7],
    linewidth = 1.5,
    alpha = 0.8,
    inherit.aes = FALSE
  ) +
  geom_point(aes(x = jobs_lost, color = worker_type), size = 3.2, alpha = 0.9) +
  geom_text(
    data = deportations |>
      filter(state %in% c("California", "Texas")) |>
      select(state, u_s_born, immigrant) |>
      pivot_longer(cols = c(u_s_born, immigrant), names_to = "worker_type", values_to = "jobs_lost") |>
      mutate(
        state = factor(state, levels = rev(p1_states_ordered)),
        label = paste0(round(jobs_lost / 1000), "K")
      ),
    aes(x = jobs_lost, y = state, label = label),
    hjust = -0.3,
    vjust = 1.4,
    size = 2.5,
    color = "gray30"
  ) +
  # Scales
  scale_x_continuous(
    labels = comma_format(suffix = "K", scale = 1 / 1000),
    expand = c(0.05, 0, 0.2, 0),
    breaks = scales::pretty_breaks(n = 5)
  ) +
  scale_color_manual(
    values = c(
      "U.S.-Born Workers" = colors$palette$us_born,
      "Immigrant Workers" = colors$palette$immigrant
    ),
    breaks = c("U.S.-Born Workers", "Immigrant Workers"),
    name = NULL
  ) +
  # Labs
  labs(
    title = "Both Worker Types Affected: No One is Spared",
    subtitle = NULL,
    x = "Jobs Lost (thousands)",
    y = NULL,
    caption = "Top 20 states by total job losses shown"
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
  ) +
  guides(color = guide_legend(
    title.position = "top",
    title.hjust = 0.5,
    nrow = 1,
    override.aes = list(size = 4)
  ))

### |-  combined plot ----
# Create an invisible spacer plot
spacer <- ggplot() +
  theme_void()

# Use it between charts
combined_plots <- (p1 + spacer + p2) +
  plot_layout(widths = c(1, 0.2, 1.1)) 

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.6),
        family = fonts$title,
        face = "bold",
        hjust = 0.,
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(1.1),
        hjust = 0,
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 12, 
  height = 8
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] yulab.utils_0.1.8  utf8_1.2.4         generics_0.1.3     renv_1.0.3        
 [5] ggplotify_0.1.2    xml2_1.3.6         stringi_1.8.4      hms_1.1.3         
 [9] digest_0.6.37      magrittr_2.0.3     evaluate_1.0.1     grid_4.4.0        
[13] timechange_0.3.0   fastmap_1.2.0      rprojroot_2.0.4    jsonlite_1.8.9    
[17] fansi_1.0.6        codetools_0.2-20   cli_3.6.4          rlang_1.1.6       
[21] commonmark_1.9.2   munsell_0.5.1      withr_3.0.2        yaml_2.3.10       
[25] tools_4.4.0        tzdb_0.5.0         colorspace_2.1-1   curl_6.0.0        
[29] gridGraphics_0.5-1 vctrs_0.6.5        R6_2.5.1           magick_2.8.5      
[33] lifecycle_1.0.4    fs_1.6.5           htmlwidgets_1.6.4  pkgconfig_2.0.3   
[37] pillar_1.9.0       gtable_0.3.6       Rcpp_1.0.13-1      xfun_0.49         
[41] tidyselect_1.2.1   rstudioapi_0.17.1  knitr_1.49         farver_2.1.2      
[45] htmltools_0.5.8.1  rmarkdown_2.29     compiler_4.4.0     markdown_1.13     
[49] gridtext_0.1.5

9. GitHub Repository

The complete code for this analysis is available in mm_2025_29.qmd.

For the full repository, click here.

10. References

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